It has become increasingly common for content, such as video assets, to be delivered to users over a network. Movies, television shows, home videos, how-to videos, and music videos are merely a few examples of the types of video assets that are currently provided over the Internet, telephone networks, intranets, cable connections, etc. Video assets that are obtained over a network can be played after the entire video assets have been downloaded, or as the video assets are being delivered (streamed).
Given the plethora of video assets that are available for consumption, it has become increasingly important to help users identify those video assets that are most likely to interest them. For example, a user will have a much better experience with a system that recommends to the user ten videos in which the user is highly interested, than with a system that merely provides tools for searching for those same ten videos among a million less interesting videos.
Various approaches have been used to recommend video assets to users, each of which has certain drawbacks. For example, one “trending” approach is to keep track of which video assets are most consumed by the user population as a whole, and to recommend those most-consumed video assets to all users. Another trending approach is to keep track of which videos are currently experiencing a surge of interest among the entire user population, and to recommend those videos to all users. These “one-shoe-fits-all” trending approaches work well for users that have interests that correspond to the average user, but not for all users.
A “video-to-video” approach is to recommend, to each user, video assets that have something in common with a video asset that has just been consumed by that user. For example, after a user has consumed a movie X, the user may be presented with recommendations for movies that are in the same genre as movie X, have the same main actors as movie X, have the same director as movie X, come from the same time period as movie X, etc. The video-to-video approach works well when the user has a narrow range of interests, but does not work so well when the user's appetite for a particular type of video has become satiated and the user now wants to consume something different.
A “user-to-video” approach for recommending video assets involves monitoring the asset consumption behavior of users over time. Based on a user's history of video consumption, the general interests of the user may be derived. Those derived interests may then be used to identify other videos in which the user may be interested, which may be offered as recommendations. The user-to-video approach works well as long as the user's consumption history accurately reflects the user's current interests. However, if the user has insufficient history, or the user is currently interested in something different than what the user has consumed in the past, user-to-video based recommendations are not particularly helpful.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.